get.landscape.FPK.MCMC: Plot posterior distribution of macroevolutionary landscapes.

In BBMV: Models for Continuous Traits Evolving in Macroevolutionary Landscapes of any Shape

Description

The function plots the median value of the macroevolutionary landscape across the posterior in a solid line and draws a polygon that streches between two quantiles of the posterior.

Usage

get.landscape.FPK.MCMC(chain, bounds, Npts = 100, burnin = 0.1, 
  probs.CI = c(0.05, 0.95), COLOR_MEDIAN = "red", COLOR_FILL = "red", 
  transparency = 0.3, main = "Macroevolutionary landscapes MCMC", 
  ylab = "N.exp(-V)", xlab = "Trait", xlim = NULL, ylim = NULL)

Arguments

chain	An data.frame object representing the output of an MCMC chain, as obtained by MH_MCMC_FPK.
bounds	The bounds on the trait interval
Npts	The number of points used in the discretization procedure.
burnin	The percentage of generations discarded as burnin.
probs.CI	A vector of the two quantiles of the posterior distribution between which samples should be considered.
COLOR_MEDIAN	The color used to plot the median macroevolutionary landscape across the posterior.
COLOR_FILL	The color used to plot the polygon that stretches between the two quantiles of the posterior.
transparency	The transparency used for plotting the polygon
main	Title of the graph.
ylab	y label of the graph.
xlab	X label of the graph.
xlim
ylim

Author(s)

F.C. Boucher

See Also

MH_MCMC_FPK

Examples

## Not run: 
# Simulate data: tree + continuous trait
library(geiger)
tree=sim.bdtree(stop='taxa',n=10) # tree with few tips for quick tests
tree$edge.length=100*tree$edge.length/max(branching.times(tree)) # rescale the tree 
# Simulate trait evolving on a macroevolutionary landscape with two peaks of equal heights
x=seq(from=-1.5,to=1.5,length.out=100)
bounds=c(min(x),max(x)) # the bounds we use for simulating: for technical purposes only
V6=10*(x^4-0.5*(x^2)+0.*x) # this is the evolutionary potential: it has two wells
TRAIT= Sim_FPK(tree,x0=0,V=V6,sigma=10,bounds=c(-5, 5)) 
# Run a MCMC chain to fit the FPK model
MCMC=MH_MCMC_FPK(tree,trait=TRAIT,bounds=c(5,5),Nsteps=10000,record_every=100,
  plot_every=100,Npts=20,pars_init=c(0,-4,-4,0,1),prob_update=c(0.2,0.25,0.25,0.25,0.05),
  verbose=TRUE,plot=TRUE,save_to='MCMC_FPK_test.Rdata',save_every=100,
  type_priors=c(rep('Normal',4),'Uniform'),
  shape_priors=list(c(0,10),c(0,10),c(0,10),c(0,10),NA),proposal_type='Uniform',
  proposal_sensitivity=c(0.1,0.1,0.1,0.1,1),prior.only=F)
get.landscape.FPK.MCMC(chain=MCMC,bounds=c(5,5),Npts=100,burnin=0.1,
  probs.CI=c(0.025,0.975),COLOR_MEDIAN='red',COLOR_FILL='red',transparency=0.3,
  main='Macroevolutionary landscapes MCMC',ylab='N.exp(-V)',xlab='Trait',
  xlim=NULL,ylim=NULL)  

## End(Not run)

BBMV documentation built on May 1, 2019, 10:26 p.m.